检索结果-内蒙古大学图书馆

Experimental investigation and machine learning prediction of thrust, fuel consumption, and emissions for micro gas turbine engine fueled with biofuel and hydrogen- A comparative study of linear regression and LSTM model

引用

Journal of the Taiwan Institute of Chemical Engineers 2025年

作者： A, Anderson Alharbi, Sulaiman Ali Chinnathambi, Arunachalam J, Rama Raju P Gavurová, Beata Department of Aeronautical Engineering Sathyabama Institute of Science and Technology Chennai600 119 India Department of Botany and Microbiology College of Science King Saud University PO Box -2455 Riyadh11451 Saudi Arabia Department of Civil Engineering Graphic Era Deemed to be University Dehradun 248002 India Technical University of Košice Faculty of Mining Ecology Process Control and Geotechnologies Letná 1/9 Košice-Sever042 00 Slovakia

Background: This study investigates the performance and emission characteristics of a micro gas turbine engine operating with the microalgae blends along with Jet-A fuel, hydrogen and ammonia. Microalgae dispersed with the Jet-A fuel at the concentration of 10% and 30%. In addition to above, both the blends also include with the hydrogen at the flow rate of 10 liters per minute (lpm) each to form B10JH (10% biodiesel with 90% Jet-A and 10 lpm hydrogen) and B30JH (30% biodiesel with 70% Jet-A and 10 lpm hydrogen). Methods: The parameters such as thrust, thrust-specific fuel consumption, and turbine inlet temperature were analyzed, along with emissions of nitrogen oxides (NOx), carbon monoxide (CO), and carbon dioxide (CO2) across a various engine speed of 30,000 to 70,000 RPM. Two machine learning models such as LR and long short-term memory, were used to predict engine performance and emissions. Significant Findings: Results showed that increasing biodiesel content led to a reduction in thrust and an increase in TSFC. However, using the hydrogen alongside the microalgae blends mitigated these effects. Gaseous fuel blends reported the highest temperatures due to the high flame speed of hydrogen. NOx emissions were generally lower for biodiesel blends compared to Jet-A, with B30J showing higher emissions at lower RPM. Hydrogen addition increased NOx emissions, particularly for the B30JH blend. CO emissions decreased with increasing engine speed, with biodiesel and hydrogen-enhanced blends showing the lowest emissions. On top of that, CO2 emissions showed inconsistent trends with engine speed, with slightly higher emissions for biodiesel blends at lower RPM and lower emissions at higher RPM, while hydrogen addition consistently reduced CO2 emissions. The long short-term memory (LSTM) model demonstrated high accuracy in predicting thrust, TSFC, TIT, NOx, and CO emissions, with R² values above 0.9 for most cases. However, both LSTM and linear regression model failed to a

关键词： Gas turbines

来源：评论

学校读者我要写书评

暂无评论

Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

引用

Soft computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Perturbations and Phase Transitions in Swarm Optimization Algorithms

arXiv

引用

arXiv 2025年

作者： Vantuch, Tomáš Zelinka, Ivan Adamatzky, Andrew Marwan, Norbert Center ENET Technical University of Ostrava Czech Republic Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Faculty of Electrical & Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science Technical University of Ostrava Czech Republic Unconventional Computing Lab UWE Bristol United Kingdom Leibniz Association Potsdam Germany

Natural systems often exhibit chaotic behavior in their space-time evolution. Systems transiting between chaos and order manifest a potential to compute, as shown with cellular automata and artificial neural networks. We demonstrate that swarm optimization algorithms also exhibit transitions from chaos, analogous to a motion of gas molecules, when particles explore solution space disorderly, to order, when particles follow a leader, similar to molecules propagating along diffusion gradients in liquid solutions of reagents. We analyze these ‘phase-like’ transitions in swarm optimization algorithms using recurrence quantification analysis and Lempel-Ziv complexity estimation. We demonstrate that converging iterations of the optimization algorithms are statistically different from non-converging ones in a view of applied chaos, complexity and predictability estimating indicators. An identification of a key factor responsible for the intensity of their phase transition is the main contribution of this paper. We examined an optimization as a process with three variable factors – an algorithm, number generator and optimization function. More than 9.000 executions of the optimization algorithm revealed that the nature of an applied algorithm itself is the main source of the phase transitions. Some of the algorithms exhibit larger transition-shifting behavior while others perform rather transition-steady computing. These findings might be important for future extensions of these algorithms. © 2025, CC BY-NC-ND.

关键词： Cellular automata

来源：评论

学校读者我要写书评

暂无评论

A Multi-Resolution Deep Learning Approach for IoT-Integrated Biomedical Signal Processing using CNN-LSTM

A Multi-Resolution Deep Learning Approach for IoT-Integrated...

引用

Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： K. Priyadharshini Ajisha Mathias R. Santhana Krishnan N Kanthimathi J. Relin Francis Raj P. Stella Rose Malar Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Electronics and Communication Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering J.P. College of Engineering Tenkasi India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The rapid advancement of Internet of Things (IoT) technology and deep learning has enabled real-time health monitoring for elderly individuals and patients. This work proposes an IoT-based health monitoring system integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to analyze physiological signals and detect anomalies effectively. A network of biomedical sensors, including ECG, EMG, EEG, heart rate, blood pressure, SpO2, temperature, accelerometer, gyroscope, and respiratory rate sensors, continuously collects health data. These sensors transmit real-time physiological signals using wireless communication protocols such as Wi-Fi, Bluetooth, LoRa, and NB-IoT, ensuring seamless data acquisition and remote monitoring. To enhance signal quality, the preprocessing stage employs Wavelet Transform (WT) for denoising biomedical signals, mitigating motion artifacts and environmental noise. Discrete Wavelet Transform (DWT) is applied to extract meaningful features while preserving critical health information. Additional preprocessing steps, including feature normalization, segmentation, and feature extraction, improve deep learning model efficiency. The proposed CNN-LSTM hybrid model leverages CNN for spatial feature extraction and LSTM for capturing temporal dependencies in time-series biomedical data. The architecture includes multiple convolutional layers with ReLU activation, batch normalization, max-pooling, bidirectional LSTM layers, dropout regularization, and an attention mechanism. Model training utilizes labeled medical datasets with an Adam optimizer and binary cross-entropy loss function. Performance evaluation considers accuracy, precision, recall, F1-score, and AUC-ROC metrics. Results demonstrate improved anomaly detection for conditions such as arrhythmias, respiratory irregularities, and early stroke indicators. Deployment in real-time health monitoring applications facilitates continuous patient assessment, fall detecti

关键词： Deep learning Medical services Feature extraction Real-time systems Convolutional neural networks Biosensors Biomedical monitoring Monitoring Medical diagnostic imaging Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Cost-effective aeration solutions for aquaculture: a study on paddle wheel and spiral aerators

引用

Aquaculture International 2025年第5期33卷 1-23页

作者： Roy, Subha M. Beg, Mirza Masum Tiyasha, Tiyasha Bhagat, Suraj Kumar Kim, Taeho Pareek, C.M. Kumar, Vinay Kumar, Reetesh Abdelrahman, Hisham A. Smart Aquaculture Research Centre Chonnam National University Yeosu 59626 South Korea Department of Fisheries Science Ramnagar College West Bengal Purba Medinipur 721453 India Department of Civil Engineering School of Engineering & Computing Dev Bhoomi Uttarakhand University Uttarkhand Dehradun 248007 India Department of Civil Engineering Faculty of Engineering & Technology Marwadi University Research Center Marwadi University Gujarat Rajkot 360003 India Department of Marine Production Management Chonnam National University Yeosu 59626 South Korea Agricultural and Food Engineering Department IIT Kharagpur West Bengal Kharagpur 721302 India Biomaterials and Tissue Engineering (BITE) Laboratory Department of Community Medicine Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai 602105 India Department of Biotechnology Galgotias University Greater Noida 201308 India Galgotias Multi-Disciplinary Research & Development Cell (G-MRDC) Galgotias University Greater Noida 201308 India Department of Veterinary Hygiene and Management Faculty of Veterinary Medicine Cairo University Giza 12211 Egypt Department of Biology Marine Biology and Environmental Science Roger Williams University Bristol 02809 RI United States

The primary objective is to select an appropriate aerator that maximizes the benefits while minimizing the operational costs of the aeration system in aquaculture operations. The choice of suitable aerators significantly impacts the cost of aquaculture operations. Therefore, this study focuses on the economic analysis of the paddle wheel aerator (PWA) and its modified counterpart, the spiral aerator (SA). The efficiency of the PWA and SA was assessed with respect to rotational speed (N), based on the efficiency a comparative economic analysis was conducted. The economics of aerators depends on the various pond sizes, initial dissolved oxygen (DO), and aerators running hours. Therefore, the total aeration cost for the chosen aerators was determined at various pond volumes (100, 200, 300, 700, 1000, 5000, and 10,000 m³ with depth of water 1.0 m) and initial DO of pond water denoted as C_P = 1 to 4 mg/L. According to market rates, the primary cost for PWA was ₹32,000, while that for SA was ₹42,000. From the findings, pond sizes in the range of 100 to 10,000 m³ PWA are more economical than SA. Therefore, a new selection method of economically feasible aerators in aquaculture pond was developed in this study. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aeration Aquaculture Cost economics Handling Editor: Brian Austin Paddle wheel aerator Spiral aerator

来源：评论

学校读者我要写书评

暂无评论

Air Traffic Management and Communication over ATN/IPS for Future Datalink Communication

Air Traffic Management and Communication over ATN/IPS for Fu...

引用

Integrated Communications, Navigation and Surveillance Conference, ICNS

作者： Emre Aydoğan Sergun Özmen Fulya Aybek Cetek Rosa María Arnaldo Valdés Raquel Delgado-Aguilera Jurado Angel Ernesto Carmona Fernández Adrián Martínez Miralles Tommaso Vendruscolo Stefano Bonelli Daniel Delahaye Supatcha Chaimatanan Feng Chen Raouf Hamzaoui Air Traffic Control Department Faculty of Aeronautics and Astronautics Eskişehir Technical University Eskişehir Türkiye Turkish Airlines Îstanbul Türkiye Department of Aerospace Systems Air Transport and Airports Universidad Politėcnica de Madrid Madrid Spain Servicios Aeronáuticos Control y Navegación Madrid Spain Deep Blue srl Rome Italy OPTIM Lab École Nationale de l 'Aviation Civile (ENAC) Toulouse France Faculty of Computing Engineering and Media De Montfort University Leicester UK

ISBN: (数字)9798331534738

ISBN: (纸本)9798331534745

The growing demand for air traffic presents challenges in air traffic management, making seamless gate-to-gate communication essential. Traditional radio frequency communication faces limitations such as weather dependency and frequency restrictions. To address these issues, data link communications have gained importance, using VHF channels, satellite systems, and ATN/IPS-based networks. This study introduces the ATMACA (Air Traffic Management and Communication Over ATN/IPS) protocol, an advanced context management framework for ATN/IPS, designed to enhance aviation communications. ATMACA integrates instant messaging and software-defined nodes to improve connectivity, session continuity, and mobility management across networks and devices. It ensures seamless user interaction, reduces pilot workload, and enhances flight safety through automated Air Traffic control (ATC) sector handoff in controller-Pilot Data Link Communications (CPDLC) and Data Link Initiation Capability (DLIC) applications. Another key innovation of the ATMACA framework is Green Route Operations (GRO), which enables real-time trajectory prediction and optimization.

关键词： Wind Temperature distribution Technological innovation Surveillance Green products User centered design Routing protocols Real-time systems Trajectory Air traffic control

来源：评论

学校读者我要写书评

暂无评论

Air Traffic Management and Communication over ATN/IPS for Future Datalink Communication

Air Traffic Management and Communication over ATN/IPS for Fu...

引用

2025 Integrated Communications, Navigation and Surveillance Conference, ICNS 2025

作者： Aydogan, Emre Ozmen, Sergun Cetek, Fulya Aybek Valdes, Rosa Maria Arnaldo Jurado, Raquel Delgado-Aguilera Fernandez, Angel Ernesto Carmona Miralles, Adrian Martinez Vendruscolo, Tommaso Bonelli, Stefano Delahaye, Daniel Chaimatanan, Supatcha Chen, Feng Hamzaoui, Raouf Eskişehir Technical University Faculty of Aeronautics and Astronautics Air Traffic Control Department Eskişehir Turkey Turkish Airlines Îstanbul Turkey Universidad Politėcnica de Madrid Department of Aerospace Systems Air Transport and Airports Madrid Spain Servicios Aeronáuticos Control y Navegación Madrid Spain Deep Blue srl Rome Italy École Nationale de l 'Aviation Civile (ENAC) OPTIM Lab Toulouse France De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom

ISBN: (纸本)9798331534738

关键词： aeronautical telecommunication networks datalink communication flight trajectory enhancement green route operations internet protocol services mobility management

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey on Intelligent Reflecting Surfaces and Unmanned Aerial Vehicles Integration for Enhanced Communication in Massive Networks

A Comprehensive Survey on Intelligent Reflecting Surfaces an...

引用

Mechatronics and Robotics Engineering (ICMRE), International Conference on

作者： Hani Attar Jafar Ababneh Mohamed Hafez Ali Rachini Ahmed Solyman Ramy M. Bahy Faculty of Engineering Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Cyber Security Department Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Engineering FEQS INTI-IU-University Nilai Malaysia Department of Computer Science and Information Technology Holy Spirit University of Kaslik (USEK) Jounieh Lebanon School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow UK Technical Research Center Cairo Egypt

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Intelligent Reflecting Surfaces (IRSs) offer a revolutionary approach to wireless communication by dynamically controlling the properties of electromagnetic waves. By manipulating wavefronts without requiring radio frequency links, IRSs can enhance spectrum and energy efficiency, reduce costs, and improve overall system performance. Unmanned Aerial Vehicles (UAVs) have garnered significant attention due to their mobility and adaptability. However, real-time data transmission between UAVs can be hindered by obstacles and security risks. By combining IRSs and UAVs, we can create unparalleled opportunities in demanding environments. Through coordinated movements and intelligent wave reflection, both systems can optimize wireless signal propagation. This paper provides a comprehensive review of IRS-assisted UAV communications. We explore existing research, introduce novel technologies and applications, and highlight potential research directions for developing low-cost and energy-efficient wireless systems. Additionally, we address current challenges and outline promising areas for future study.

关键词： NOMA Reviews Wireless networks Autonomous aerial vehicles Energy efficiency Real-time systems Security Millimeter wave communication Visible light communication Optical surface waves

来源：评论

学校读者我要写书评

暂无评论

Early detection of Alzheimer’s disease progression stages using hybrid of CNN and transformer encoder models

引用

Scientific Reports 2025年第1期15卷 1-22页

作者： Almalki, Hassan Khadidos, Alaa O. Alhebaishi, Nawaf Senan, Ebrahim Mohammed Department of Information Technology College of Technology for Communications and Information Technical and Vocational training Corporation Riyadh Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science College of Applied Sciences Hajjah University Hajjah Yemen Department of Artificial Intelligence Faculty of Computer Science and Information Technology Al-Razi University Sana’a Yemen

Alzheimer’s disease (AD) is a neurodegenerative disorder that affects memory and cognitive functions. Manual diagnosis is prone to human error, often leading to misdiagnosis or delayed detection. MRI techniques help visualize the fine tissues of the brain cells, indicating the stage of disease progression. Artificial intelligence techniques analyze MRI with high accuracy and extract subtle features that are difficult to diagnose manually. In this study, a modern methodology was designed that combines the power of CNN models (ResNet101 and GoogLeNet) to extract local deep features and the power of Vision Transformer (ViT) models to extract global features and find relationships between image spots. First, the MRI images of the Open Access Imaging Studies Series (OASIS) dataset were improved by two filters: the adaptive median filter (AMF) and Laplacian filter. The ResNet101 and GoogLeNet models were modified to suit the feature extraction task and reduce computational cost. The ViT architecture was modified to reduce the computational cost while increasing the number of attention vertices to further discover global features and relationships between image patches. The enhanced images were fed into the proposed ViT-CNN methodology. The enhanced images were fed to the modified ResNet101 and GoogLeNet models to extract the deep feature maps with high accuracy. Deep feature maps were fed into the modified ViT model. The deep feature maps were partitioned into 32 feature maps using ResNet101 and 16 feature maps using GoogLeNet, both with a size of 64 features. The feature maps were encoded to recognize the spatial arrangement of the patch and preserve the relationship between patches, helping the self-attention layers distinguish between patches based on their positions. They were fed to the transformer encoder, which consisted of six blocks and multiple vertices to focus on different patterns or regions simultaneously. Finally, the MLP classification layers classify each

关键词： Alzheimer’s disease CNN-ViT GoogLeNet Hybrid Models ResNet101 Vision transformer

来源：评论

学校读者我要写书评

暂无评论

A Smart IoT-Enabled Wildfire Monitoring and Early Detection System using Convolutional Neural Networks and Sensor Data Analysis

A Smart IoT-Enabled Wildfire Monitoring and Early Detection ...

引用

Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： U.V. Anbazhagu A. Alice Blessie R. Santhana Krishnan G. Yamini J. Relin Francis Raj P. Sundaravadivel Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Government College of Engineering Tirunelveli India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of English Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Wildfires pose a significant threat to ecosystems, biodiversity, and human settlements, with climate change and deforestation exacerbating the frequency and intensity. Conventional forest fire detection methods, including satellite-based monitoring and ground surveillance, suffer from limitations such as delayed detection, high false alarm rates, and dependency on manual intervention. Existing sensor-based detection systems often lack integration with intelligent decision-making frameworks, leading to inefficient fire prevention strategies. The absence of real-time processing and predictive capabilities further reduces the effectiveness of current methodologies in mitigating wildfire risks. This work presents an IoT-based deep learning approach utilizing Convolutional Neural Networks (CNNs) for real-time forest fire detection and prevention. Data is collected from satellite images, drone feeds, and IoT sensors, including temperature, humidity, gas concentration, and infrared readings. Advanced data preprocessing techniques, including image augmentation, normalization, and feature extraction, enhance the robustness of the model. A CNN-based classification model is implemented to analyze fire patterns and assess risk levels. The system is trained using sensor data and image datasets, ensuring high detection accuracy with minimal false positives. The key features of this methodology include real-time wildfire detection, automated risk assessment, and deployment on IoT edge devices for immediate decision-making. The integration of sensor-driven insights with deep learning improves early detection capabilities while reducing reliance on manual surveillance. Efficient data handling and cloud-based alert mechanisms enable proactive measures against fire outbreaks. The proposed system enhances wildfire monitoring by ensuring fast response times, improved accuracy, and scalable deployment in fire-prone regions.

关键词： Deep learning Wildfires Accuracy Prevention and mitigation Surveillance Decision making Manuals Real-time systems Satellite images Time factors Climate change Fires Detection algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：